用于s参数预测的二维谱转置卷积神经网络

2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) Pub Date : 2022-10-09 DOI:10.1109/EPEPS53828.2022.9947109

Yiliang Guo, Xingchen Li, Madhavan Swaminathan

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引用次数: 2

摘要

在包装问题中，s参数预测是必要的。机器学习方法导致维度相关的挑战，我们在这里通过使用二维核的频谱转位卷积神经网络来解决。结果表明，使用53.7%的参数后，归一化均方误差(NMSE)下降了0.002。

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2D Spectral Transposed Convolutional Neural Network for S-Parameter Predictions

In packaging problems, S-parameter predictions are necessary. Machine learning methods lead to dimensionality related challenges which we address here through spectral trans-posed convolutional neural network using 2D kernels. Results show that Normalized Mean-squared Error (NMSE) dropped 0.002 by using 53.7% of the parameters.

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2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

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